Arid areas are particularly susceptible to soil erosion due to long dry periods and sudden heavy downpours. This study investigates the aggregate size distribution and aggregate stability of twelve tilled fallow areas of Hyderabad district, Sindh, Pakistan. This study determined aggregate size distribution by dry sieving to evaluate the seedbed condition and aggregate stability using wet sieving to assess the susceptibility of tilled fallow areas to soil erosion. The aggregate size distribution of the soils of the selected areas was highly variable. Gulistan-e-Sarmast had the largest number of clods (51.0%) followed by Kohsar (49.0%), Latifabad # 10 (41.10%) and Daman-e-Kohsar (39.0%). Fazal Sun City, the left side of the Indus River, the Village Nooral Detha and the left side of the Abdullah Sports city had a greater number of large (>8.0 mm) and small aggregates (<0.5 mm). The optimum aggregate size distribution was found in the left side of the channel, which had the largest number of aggregates (50.50%) in the 0.5–8.0 mm sieve size range. Maximum aggregate stability (AS) was found in Gulistan-e-Sarmast (46%), Kohsar (42%) and Latifabad # 10 (34%), while all other soils had minimum aggregate stability (<14%). The minimum aggregate stabilities demonstrate that the tilled fallow areas of Hyderabad district are highly susceptible to erosion. Therefore, the present study suggests investigating potential ways to enhance the aggregate stabilities of soils.

While assessing the effects of climate change at global or regional scales, local factors responsible for climate change are generalized, which results in the averaging of effects. However, climate change assessment is required at a micro-scale to determine the severity of climate change. To ascertain the impact of spatial scales on climate change assessments, trends and shifts in annual and seasonal (monsoon and non-monsoon), rainfall and temperature (minimum, average and maximum) were determined at three different spatial resolutions in India (Ajmer city, Ajmer District and Rajasthan State). The Mann–Kendall (MK), MK test with pre-whitening of series (MK–PW), and Modified Mann–Kendall (MMK) test, along with other statistical techniques were used for the trend analysis. The Pettitt–Mann–Whitney (PMW) test was applied to detect the temporal shift in climatic parameters. The Sen’s slope and % change in rainfall and temperature were also estimated over the study period (35 years). The annual and seasonal average temperature indicates significant warming trends, when assessed at a fine spatial resolution (Ajmer city) compared to a coarser spatial resolution (Ajmer District and Rajasthan State resolutions). Increasing trend was observed in minimum, mean and maximum temperature at all spatial scales; however, trends were more pronounced at a finer spatial resolution (Ajmer city). The PMW test indicates only the significant shift in non-monsoon season rainfall, which shows an increase in rainfall after 1995 in Ajmer city. The Kurtosis and coefficient of variation also revealed significant climate change, when assessed at a finer spatial resolution (Ajmer city) compared to a coarser resolution. This shows the contribution of land use/land cover change and several other local anthropogenic activities on climate change. The results of this study can be useful for the identification of optimum climate change adaptation and mitigation strategies based on the severity of climate change at different spatial scales.

Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM) was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE), Non Dominated Sorting Genetic Algorithm (NSGA II) and Simulated Annealing (SA). It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.

Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km2) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.

A strategic vision to ensure an adequate, safe and secure drinking water supply presents a challenge, particularly for such a small country as Jordan, faced with a critical supply-demand imbalance and a high risk of water quality deterioration. In order to provide sustainable and equitable long-term water management plans for the future, current and future demands, along with available adaptation options should be assessed through community engagement. An analysis of available water resources, existing demands and use per sector served to assess the nation’s historic water status. Taking into account the effect of both population growth and rainfall reduction, future per sector demands were predicted by linear temporal trend analysis. Water sector vulnerability and adaptation options were assessed by engaging thirty five stakeholders. A set of weighed-criterions were selected, adopted, modified, and then framed into comprehensive guidelines. A quantitative ratio-level approach was used to quantify the magnitude and likelihood of risks and opportunities associated with each proposed adaptation measure using the level of effectiveness and severity status. Prioritization indicated that public awareness and training programs were the most feasible and effective adaptation measures, while building new infrastructure was of low priority. Associated barriers were related to a lack of financial resources, institutional arrangements, and data collection, sharing, availability, consistency and transparency, as well as willingness to adapt. Independent community-based watershed-vulnerability analyses to address water integrity at watershed scale are recommended.

Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference sys-tems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydro-logical data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the pre-sent research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.